Domain Generalization Algorithms. These techniques use the same set of features (i.e., the same feature matrix) as the input. We pick representative ones to cover major directions of domain generalization [1]}:
1) ERM (Empirical Risk Minimization) [2]}. We implement multiple architectures with ERM: ERM-1D-CNN, ERM-2D-CNN, ERM-LSTM, ERM-Transformer;
2) Mixup [3]};
3) IRM (Invariant Risk Minimization) [4]};
4) DANN (Domain-Adversarial Neural Network) [5]}. We test both using dataset as a domain (DANN-Dataset as Domain), and person as a domain (DANN-P);
5) CSD (Common Specific Decomposition) [6]}. Similarly, we also test CSD-D and CSD-P;
6) MLDG (Meta-Learning for Domain Generalization) [7]}, with MLDG-D, and MLDG-P;
7) MASF (Model-Agnostic Learning of Semantic Features) [8]}, with MASF-D, and MASF-P;
8) Siamese Network [9]};
9) Reorder [10]}, a self-supervised learning-based algorithm that leverages order reconstruction of a shuffled sequence as the pre-text task.
Algorithms from 2-9 use the same 1D-CNN as the backbone.
